面向IoT的两级多接入边缘计算节能卸载策略

多接入边缘计算（multi-access edge computing，MEC）技术将计算和存储资源下沉到网络边缘，可大幅提高物联网（Internet of things，IoT）系统的计算能力和实时性。然而，MEC往往面临计算需求增长和能量受限的约束，高效的计算卸载及能耗优化机制是MEC技术中重要的研究领域。为保证计算效率的同时最大程度提升计算过程中的能效，提出了两级边缘节点（edge nodes，ENs）中继网络模型，并设计了一种计算资源及信道资源联合优化的最优能耗卸载策略算法（optimal energy consumption algorithm，OECA）。将MEC中的能效建模为0-1背包问题；以最小化系统总体能耗为目标，系统自适应地选择计算模式和分配无线信道资源；在Python环境下仿真验证了算法性能。仿真结果表明，相比于基于有向无环图的卸载策略算法（directed acyclic graph algorithm，DAGA），OECA可将网络容量提升18.3%，能耗缩减13.1%。

Two-Level Multi-Access Edge Computing Energy-Saving Offloading Strategy for IoT

Multi-access edge computing（MEC） technology sinks computing and storage resources to the edge of the network, which can greatly improve the computing power and real-time performance of the Internet of things（IoT） system. However, MEC is often constrained by the growth of computing demand and energy constraints. Thus, efficient computing offloading and energy consumption optimization mechanism is an important research direction in MEC technology. To ensure the computing efficiency while maximizing the energy efficiency in the computing process, a two-level edge nodes（ENs） relay network model is proposed, and an optimal energy consumption algorithm（OECA） for joint optimization of computing resources and channel resources is designed. Firstly, the energy efficiency in MEC is modeled as a 0-1 knapsack problem. Secondly, the system adaptively selects the computing mode and allocates wireless channel resources, aiming at minimizing the overall energy consumption of the system. Finally, the OECA algorithm is simulated and verified in Python environment. The simulation results show that OECA can increase the network capacity by 18.3% and reduce the energy consumption by 13.1% compared with the offloading strategy algorithm based on directed acyclic graph algorithm（DAGA）.